Systems and methods for adaptive replanning based on multi-modality imaging

ABSTRACT

Systems and methods directed to adaptive radiotherapy planning are provided. In some aspects, provided system and method include producing synthetic images from magnetic resonance data using relaxometry maps. The method includes applying corrections to the data and generating relaxometry maps therefrom. In other aspects, a method for adapting a radiotherapy plan is provided. The method includes determining an objective function based on dose gradients from an initial dose distribution, and generating an optimized plan based on updated images, using aperture morphing and gradient maintenance algorithms without need for organ-at-risk contouring. In yet other aspects, a method for obtaining 4D MR imaging using a temporal reshuffling of data acquired during normal breathing, a method for deformable image registration using a sequentially applied semi-physical model regularization method for multimodality images, and a method to generate 4D plans using an aperture morphing algorithm based on 4D CT or 4D MR imaging are provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/108,188, filed on Jun. 24, 2016, which is a national stage ofInternational Patent Application No. PCT/US2014/072645, filed Dec. 30,2014 which claims the benefit of U.S. Provisional Patent ApplicationSer. No. 61/922,343, filed on Dec. 31, 2013, and entitled “SYSTEMS ANDMETHODS FOR ADAPTIVE REPLANNING BASED ON MULTI-MODALITY IMAGING.”

BACKGROUND

The present disclosure relates generally to tools, systems and methodsfor use in radiotherapy treatment delivery and, in particular, tosystems and methods for multi-modality imaging-based adaptive planning.

Radiation therapy (RT) has gone through a series of technologicalrevolutions in the last few decades. With intensity modulated RT (IMRT),it became possible to produce highly conformal dose distributions,whereby the bulk radiation dose is delivered within the extent of atumor. These techniques utilize 3D anatomic and biological informationextracted from images of various types (e.g., CT, MRI, PET) acquired afew days prior to the first treatment. However, the locations, shapesand sizes as well as biological properties of the tumor(s), and normalanatomy have been found to change during the course of treatment,primarily due to daily positioning uncertainties of various ROIs andanatomic, physiological and/or clinical factors. The latter includetumor shrinkage, weight loss, volumetric changes in normal organs,non-rigid variations in different bony structures. The traditionalassumption that the anatomy discerned from 3D CT images acquired forplanning purposes prior to the treatment course is applicable for everyfraction may not adequately account for the inter-fractional changes andmay limit the ability to fully exploit the potential of highly conformaltreatment modalities such as IMRT. This improved capability in doseconformality necessitates better localization of target and organs atrisks (OAR), as well as the ability to adapt to inter- orintra-fractional changes, both in the design and in delivery oftreatments.

Accurate delivery of radiation therapy necessitates, as a prerequisite,high fidelity, 3D, anatomical images of the patient. For close to twodecades, RT planning has utilized CT images as a basis for radiationdose calculation. Due to the linear relationship between Hounsfieldunits and underlying tissue electron density, CT images permit avoxel-wise correction of radiation dose for differences in tissueattenuation, as well as generation of beam's eye view digitallyreconstructed radiographs (DRRs) for treatment verification. Theformation of image contrast in CT is primarily due to photoelectricinteractions. This effect results in high contrast between tissues ofconsiderably different densities (e.g., bone, lung, and soft tissue).However, adjacent soft tissues (e.g., in brain or abdominal regions) donot possess substantial density differences. The ensuing poor softtissue contrast on CT images makes delineation of both targets andcritical structures extremely challenging. This inability to reliablydelineate tumor targets and proximal critical structures on CT imageshas significant clinical consequences, in that it demands the use oflarger margins, which precludes the ability to safely escalate radiationdoses due to toxicity constraints of critical structures.

Magnetic resonance imaging (MRI) provides powerful imaging capabilitiesfor cancer diagnosis and treatment. Contrary to CT, MRI is non-ionizing,offers superior soft tissue contrast, and provides a wide array offunctional contrast forming mechanisms to characterize tumor physiology.However, unlike CT, the MRI signal bears no direct relationship toelectron density, which prevents MRI from being used as a basis for dosecalculation. In addition, MR images can be confounded by spatialdistortions arising from gradient nonlinearities as well asoff-resonance effects. These distortions can be severe, ranging up toseveral centimeters. Moreover, MR images commonly exhibit regions ofnon-uniform image intensities, which can introduce inaccuracies in imageregistration and image segmentation algorithms frequently employedduring radiation treatment planning and delivery. These non-uniformimage intensities arise out of inhomogeneities in the B1+(RF transmit)field and B1− (RF receive) field when phased-array coils are used forsignal reception. Consequently, despite the obvious soft tissue contrastadvantages MRI provides, these current issues (e.g., lack of electrondensity information, sources of geometric distortion, and signalnon-uniformities) have hindered the establishment of MRI as a primaryimaging modality in radiation oncology.

Adaptive Radiation Therapy (ART) is a state-of-the-art approach thatuses a feedback process to account for patient-specific anatomic and/orbiological changes during the treatment, thus, delivering highlyindividualized radiation therapy for cancer patients. Basic componentsof ART include: (1) detection of anatomic and biological changes, oftenfacilitated by multi-modality images (e.g., CT, MRI, PET), (2) treatmentplan optimization to account for the patient-specific spatialmorphological and biological changes with consideration of radiationresponses, and (3) technologies to precisely deliver the optimized planto the patient. Interventions of ART may consist of both online andoffline approaches.

The inter- and intra-fractional variations, if not accounted for, couldresult in sub-optimal dose distributions and significant deviations fromthe original plan, with potentially negative impact in treatmentefficiency. Recently, image guided RT (IGRT) has been used widely tocorrect (eliminate or reduce) the deteriorating effects of theinter-fractional variations. A wide range of correction strategies hasbeen developed based on the available IGRT technologies. The correctionmethods can be generally classified as “online” or “offline” approaches.Corrections to patient treatment parameters that are performed rightafter the daily patient information is acquired and before the dailytreatment dose is delivered are classified as “online” corrections. Thisis in contrast to an “offline” correction where the corrective action ismade after the daily treatment has been delivered affecting thetreatments of subsequent days. Therefore, when an online correction isapplied, the delivered daily dose will be the corrected one using thevery latest patient set up and anatomic information.

When a patient is set up for each fraction, the anatomy may be differentfrom the one used for the initial treatment planning. Typically thedeviations that are most harmful are the so called “systematic” ones,which are also relatively easier to account for by either an offline oronline correction strategy. The random component of the deviations,although sometimes less harmful than the systematic deviations, aregenerally difficult to be fully accounted for and requires an onlinecorrection strategy. One of the advantages of online correctionstrategies over the offline methods is that online strategies cancorrect for both systematic and random variations. In addition, offlinecorrections may not be applicable to a course of therapy with a smallnumber of treatment fractions, such as hypo-fractionation orstereotactic body RT (SBRT).

The chief challenge for an online correction strategy is that it needsto be performed within an acceptable time-frame while the patient islying on the treatment table in the treatment position. This requirementlimits a variety of corrective actions that are used as onlinestrategies in today's technology. Consequently, online correction ofinter-fractional variations by repositioning the patient based on imagesacquired immediately before the treatment delivery is the currentstandard practice for IGRT. Online repositioning strategies practiced inmost clinics so far are limited to correct for translational shiftsonly, failing to account for rotational errors, volume changes anddeformations of targets and OARs, and independent motions betweendifferent targets/OARs. This is in spite of the fact that the currenttechnology provides enough information to perform much more detailedmodifications to the daily treatment than simple translational shifts.In principle, the data required to generate a new treatment plan for theday is available in today's CT-based IGRT practice, but by using thedata only for shifting the patient, the full potential of the IGRT isnot exploited.

There has been ongoing research to extend online corrections from thetable shifting to modifications that can correct for changing anatomy,such as organ rotations and deformations. For example, correction forrotations in addition to translations has been implemented, at leastpartially, in several technologies (e.g., Tomotherapy). However, otherinter-fraction variations may not accounted for and remain a majorconcern. Such variations can be handled by online re-planning methods,including rapid online plan modifications and full-blown planre-optimization based on anatomy of the day. For example, quickadjustment of beam aperture shapes and weights based on the CT of theday (the anatomy of the day) is an online plan modification methodfacilitated by advances in computer technology, which allowscomputationally intensive operations to be performed within a reasonabletime frame. Other examples include GPU-based full-blown re-optimizationand adaptation based on pre-computed plan libraries. Consequently,online correction schemes that are more comprehensive than onlinerepositioning are beginning to move into the clinic. However, suchonline re-planning approaches still present challenges since theyrequire delineation of targets and OARs, which is time a consuming anddifficult process to fully automate. As such, manual delineation, oreven manual contour validation, on single or multi-modality imaging isthe main bottleneck for an online re-planning process to be performedwithin a few minutes.

Therefore, given the above, there is a need for systems and methodsemploying multi-modality images for use in adapting and deliveringradiotherapy treatments in clinically feasible time frames.

SUMMARY

The present disclosure overcomes the aforementioned drawbacks byproviding systems and methods directed to adaptive radiotherapyplanning.

In accordance with one aspect of the disclosure, a system for developinga radiotherapy treatment plan is provided. The system includes a datastorage device configured to hold MR image data acquired by an MRIsystem, and at least one processor configured to receive the MR imagedata from the data storage device, apply corrections to the MR imagedata to produce a series of corrected image data, and assemble theseries of corrected image data to generate a set of relaxometry maps.The at least one processor is also configured to perform a segmentationof a plurality of regions of interest using the set of relaxometry maps,classify the plurality of regions of interest, using the set ofrelaxometry maps, to yield a plurality of classified structures, andassign electron density values to the classified structures using anassignment process. The at least one processor is further configured togenerate, using the electron density values of the classifiedstructures, a set of corrected synthetic electron density images, andperform a dose calculation using the corrected synthetic electrondensity images to develop a radiotherapy treatment plan.

In accordance with another aspect of the disclosure, a method forproducing synthetic images for use in a radiotherapy treatment isprovided. The method includes directing a magnetic resonance imaging(MRI) system to acquire a plurality of image data for use in aradiotherapy process, receiving the plurality of acquired image datafrom the MRI system, and applying corrections to the MR image data toproduce a series of corrected image data. The method also includesassembling the series of corrected image data to generate a set ofrelaxometry maps, performing a segmentation of a plurality of regions ofinterest using the set of relaxometry maps, and classifying theplurality of regions of interest, using the set of relaxometry maps, toyield a plurality of classified structures. The method further includesassigning electron density values to the classified structures using anassignment process, generating, using the electron density values of theclassified structures, a set of corrected synthetic electron densityimages, and optionally performing a dose calculation using the correctedsynthetic electron density images to develop a radiotherapy treatmentplan

In accordance with yet another aspect of the disclosure, a method foradapting a radiotherapy treatment plan is provided. The method includesproviding an initial radiotherapy plan to be adapted according to anupdated image set, the initial radiotherapy plan having a radiation dosedistribution, determining a plurality of dose gradients using theradiation dose distribution, and defining an optimization objectiveusing the dose gradients. The method also includes receiving the updatedimage set, generating, using the updated image set, an updated set ofcontours representative of an updated target volume, and forming a setof partial rings using the updated set of contours, the set of partialrings arranged about the updated set of contours representative of theupdated target volume. The method further includes performing a planoptimization using the optimization objective and the set of partialrings, and generating a report representative of an adapted radiotherapyplan obtained using the plan optimization.

The foregoing and other advantages of the disclosure will appear fromthe following description.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a flowchart setting forth steps associated with the processof providing radiotherapy treatment to a patient.

FIG. 2 is a schematic of an example MRI system for use in accordancewith the present disclosure.

FIG. 3 is a flowchart setting forth steps associated with operation ofthe MRI system of FIG. 2 to generate synthetic electron density images,in accordance with the present disclosure.

FIG. 4 is a block diagram of an example image acquisition andpost-processing using the system of FIG. 2, in accordance with thepresent disclosure.

FIG. 5 shows an example of brain T1 parameter maps from a healthyvolunteer generated with and without B1+ correction, in accordance withthe present disclosure.

FIG. 6 is a graphical illustration of electron density to HounsfieldUnit conversion table from clinical radiation treatment planning systemused to convert synthetic electron density image to synthetic CT image,in accordance with the present disclosure.

FIG. 7 shows example synthetic electron density and synthetic CT imagesobtained from the pelvis of a healthy volunteer, in accordance with thepresent disclosure.

FIG. 8 shows an example volume-modulated arc therapy plan calculatedusing synthetic CT images with and without heterogeneity corrections, inaccordance with the present disclosure.

FIG. 9 shows an illustration of a deformable transformation between afixed and moving image, in accordance with the present disclosure.

FIG. 10 shows an example of bending energy limited diffeomorphism(BELD)-regularized deformable image registration (DIR) for a breastcancer case with the patient in supine position, in accordance with thepresent disclosure.

FIG. 11 shows an example of BELD-regularized DIR for a breast cancercase with the patient in prone position, in accordance with the presentdisclosure.

FIG. 12 shows example images illustrating multi-modality contouredstructures resulting from application of deformation fields, inaccordance with the present disclosure.

FIG. 13 shows a graphical example demonstrating the effect of avariable-kernel smoothing technique on registration accuracy, inaccordance with the present disclosure.

FIG. 14 shows image results from a registration using a variable-kernelsoothing applied to pelvic CT scans, in accordance with the presentdisclosure.

FIG. 15 shows an example illustrating a registration between CT and highfrequency ultrasound (HFUS) images for a patient with a superficialbasal cell carcinoma.

FIG. 16 is a flowchart setting forth steps associated with a mode ofoperation for a gradient-maintenance algorithm, in accordance with thepresent disclosure.

FIG. 17 shows a diagram comparing online re-planning schemes using aconventional and gradient-maintenance approach, in accordance with thepresent disclosure.

FIG. 18 shows example dose distributions from an image guidedradiotherapy (IGRT) repositioning, a conventional full-blownoptimization based on a full contour set, and a gradient-maintenance, inaccordance with the present disclosure.

FIG. 19 shows example dose volume histograms for the three strategies ofFIG. 18 used to address inter-fractional variations in daily treatmentsfor a prostate cancer case.

FIG. 20 illustrates an offline optimization, in accordance with thepresent disclosure.

FIG. 21 shows a comparison between image intensities obtained usingvarious MRI scanners and scanning conditions, and subject to correctionsand normalization, in accordance with aspects the present disclosure.

FIG. 22 shows a diagram for a retrospective reconstruction ofphase-resolved 4D MR images in accordance with aspects of the presentdisclosure.

FIG. 23 shows examples of partially concentric rings utilized in a fastonline re-planning approach, in accordance with aspects of the presentdisclosure.

DETAILED DESCRIPTION

Current clinical practice associated with delivery of any one or moreradiotherapy treatments involves complex, carefully coordinatedworkflows, employing a multitude of tools and systems designed toachieve maximal patient benefit. FIG. 1 shows an example flowchartillustrating the general steps associated with such a radiotherapytreatment process. The process 100 typically begins at process block 10,where medical image information is generated from a patient typicallyusing of a variety of imaging approaches, either for diagnostic ortreatment purposes. For example, as illustrated in FIG. 1, this caninclude use of computed tomography (CT) devices, magnetic resonanceimaging (MRI) devices, positron emission tomography (PET) imagingdevices, ultrasound (US) imaging devices, and so forth. Informationobtained from imaging serves two main purposes in radiotherapy (RT).First, it is used to determine true three-dimensional positions andextent of targeted diseased tissues relative to adjacent criticalstructures or objects at risk (OARs), which typically have radiationdose toxicity constraints. Second, it is used to localize such targetsand OARs, for example, during a daily treatment setup, in order to makeany treatment adjustments prior to radiation delivery.

In general, CT images are the standard imaging modality utilized fortreatment planning. In a simulation stage, a patient is immobilized andimaged with reference marks that establish specific coordinates, whichmay subsequently be reproduced in a treatment system during radiationdelivery. The acquired images are then utilized in a planning stage togenerate a treatment plan. In addition to CT images, other imagingmodalities offer improved contrast and other useful information relatedto anatomical features and biological processes of normal and diseasedtissues or structures. In particular, MRI is non-ionizing, and offerssuperior soft tissue contrast compared to CT, while providing a widearray of functional contrast forming mechanisms to characterize tumorphysiology. However, in contrast to CT images, MR images lack electrondensity (ED) information, which is necessary for radiation dosecalculations. Hence, such non-CT images need to be processed orsynthesized to be “CT-like” in order to find use in RT planning anddelivery.

Previously, different approaches have been explored to transform MRimages into CT-like images, namely: 1) manual bulk electron densityassignment, 2) deformable image registration, 3) voxel-basedclassification, 4) use of ultra-short echo time (UTE) images, and 5) useof Dixon images. Each of these methods has its drawbacks, as describedbelow.

In manual bulk density assignment methods, tissue contours are manuallyconstructed on a patient's MR image and bulk electron densities are thenassigned to each of the manually constructed structures. The major issuewith this method is that it suffers from large intra- and inter-observervariability. Variability in observer contouring, particularly at tissueinterfaces, has been shown to produce severe errors in the computed dosedistribution. In deformable image registration (DIR) methods, a vectordisplacement field (VDF) is typically estimated by warping a patient'sMRI image to a reference MRI image. The VDF is then applied to warp a“gold standard” CT image back to the vector space of the patient's MRIimage. However, the accuracy of this method is subject to the accuracyof DIR and, particularly, can be challenged in patients with atypicalanatomy. In addition, non-uniform MR image intensities, induced byB1+(RF transmit) and B1− (RF receive) inhomogeneities can challenge theaccuracy of intensity-based DIR algorithms. Moreover, variations ingrayscale signal intensities, common in conventional, magnitude MRimages, can also challenge the accuracy and robustness of intensitybased DIR algorithms. In voxel-based classification methods, each voxelis classified into a specific tissue type, followed by direct conversionof MRI gray scale intensities to Hounsfield units or electron densities.However, non-uniform image intensities and variations in conventionalmagnitude MR image grayscale intensities can again confound the accuracyof tissue classification with voxel-based methods. Although UTEsequences may offer advantages for imaging bone, they lack contrast forother tissue types and are not yet commercially available on allclinical MRI scanners. Finally, misregistration of multi-echo Dixonimages can confound image segmentation, resulting in blurred structureboundaries.

In addition to having accurate and usable image information, the presentdisclosure recognizes that CT-like or CT substitute images need tosatisfy some additional conditions to find practical use in RTtreatment. Specifically, such images necessitate 3D volumetric coveragecomposed of contiguous slices to allow for accurate and completestructure delineation for treatment plan evaluation using dose-volumehistograms (DVH's). Also, full patient cross-sections within a field ofview (FOV) are needed for accurate dose calculation, because suchcalculations involve distances between a radiation source and apatient's skin. In addition, for accurate treatment verification throughimproved digitally reconstructed radiograph (DRR) image quality, slicesthicknesses of 3 mm or less are advantageous, while accurate imageregistration and image segmentation algorithms may benefit from highspatial resolution (for example, less than 1 mm²) and image uniformity.With respect to imaging of body regions susceptible to respiratory,peristaltic, and cardiac motion, appropriate acquisition speed isdesirable for artifact-free images, while accurate anatomicaldelineation and dose accumulation calls for high geometric fidelity.

Conditions imposed by the nature of RT treatment have limited use ofnon-standard images to qualitative assessment. In particular, despitethe advantages in soft tissue contrast that MRI provides, drawbacks suchas the lack of electron density information, geometric distortion, andsignal non-uniformities, have hindered the establishment of MR imagingas a primary imaging modality in RT treatment.

Therefore, it is one aspect of the present disclosure to provide systemsand methods that successfully implement non-ionizing MR imaging into aRT treatment process. Specifically, such systems and methods may be usedto generate synthetic ED images that overcome shortcomings of priorapproaches, and are practical for use in RT treatment planning. As willbe described, synthetic ED images may be obtained using relaxometryparameter maps, with the images being fully corrected for known sourcesof error, including magnetic field (B0) errors, RF transmit field (B1+)inhomogeneity errors, and geometric distortion errors from gradientnonlinearity.

Referring specifically to FIG. 2, an example of a magnetic resonanceimaging (MRI) system 200 is illustrated. The MRI system 200 includes anoperator workstation 102, which will typically include a display 104,one or more input devices 106, such as a keyboard and mouse, and aprocessor 108. The processor 108 may include a commercially availableprogrammable machine running a commercially available operating system.The operator workstation 102 provides the operator interface thatenables scan prescriptions to be entered into the MRI system 200. Ingeneral, the operator workstation 102 may be coupled to four servers: apulse sequence server 110; a data acquisition server 112; a dataprocessing server 114; and a data store server 116. The operatorworkstation 102 and each server 110, 112, 114, and 116 are connected tocommunicate with each other. For example, the servers 110, 112, 114, and116 may be connected via a communication system 117, which may includeany suitable network connection, whether wired, wireless, or acombination of both. As an example, the communication system 117 mayinclude both proprietary or dedicated networks, as well as opennetworks, such as the internet.

The pulse sequence server 110 functions in response to instructionsdownloaded from the operator workstation 102 to operate a gradientsystem 118 and a radiofrequency (“RF”) system 120. Gradient waveformsnecessary to perform the prescribed scan are produced and applied to thegradient system 118, which excites gradient coils in an assembly 122 toproduce the magnetic field gradients and used for position encodingmagnetic resonance signals. The gradient coil assembly 122 forms part ofa magnet assembly 124 that includes a polarizing magnet 126 and awhole-body RF coil 128.

RF waveforms are applied by the RF system 120 to the RF coil 128, or aseparate local coil (not shown in FIG. 2), in order to perform theprescribed magnetic resonance pulse sequence. Responsive magneticresonance signals detected by the RF coil 128, or a separate local coil(not shown in FIG. 2), are received by the RF system 120, where they areamplified, demodulated, filtered, and digitized under direction ofcommands produced by the pulse sequence server 110. The RF system 120includes an RF transmitter for producing a wide variety of RF pulsesused in MRI pulse sequences. The RF transmitter is responsive to thescan prescription and direction from the pulse sequence server 110 toproduce RF pulses of the desired frequency, phase, and pulse amplitudewaveform. The generated RF pulses may be applied to the whole-body RFcoil 128 or to one or more local coils or coil arrays (not shown in FIG.2).

The RF system 120 also includes one or more RF receiver channels. EachRF receiver channel includes an RF preamplifier that amplifies themagnetic resonance signal received by the coil 128 to which it isconnected, and a detector that detects and digitizes the quadraturecomponents of the received magnetic resonance signal. The magnitude ofthe received magnetic resonance signal may, therefore, be determined atany sampled point by the square root of the sum of the squares of thecomponents:

M=√{square root over (I ² +Q ²)}  (1);

and the phase of the received magnetic resonance signal may also bedetermined according to the following relationship:

$\begin{matrix}{\phi = {{\tan^{- 1}\left( \frac{Q}{I} \right)}.}} & (2)\end{matrix}$

The pulse sequence server 110 also optionally receives patient data froma physiological acquisition controller 130. By way of example, thephysiological acquisition controller 130 may receive signals from anumber of different sensors connected to the patient, such aselectrocardiograph (“ECG”) signals from electrodes, or respiratorysignals from respiratory bellows or other respiratory monitoring device.Such signals are typically used by the pulse sequence server 110 tosynchronize, or “gate,” the performance of the scan with the subject'sheart beat or respiration.

The pulse sequence server 110 also connects to a scan room interfacecircuit 132 that receives signals from various sensors associated withthe condition of the patient and the magnet system. It is also throughthe scan room interface circuit 132 that a patient positioning system134 receives commands to move the patient to desired positions duringthe scan.

The digitized magnetic resonance signal samples produced by the RFsystem 120 are received by the data acquisition server 112. The dataacquisition server 112 operates in response to instructions downloadedfrom the operator workstation 102 to receive the real-time magneticresonance data and provide buffer storage, such that no data is lost bydata overrun. In some scans, the data acquisition server 112 does littlemore than pass the acquired magnetic resonance data to the dataprocessor server 114. However, in scans that require information derivedfrom acquired magnetic resonance data to control the further performanceof the scan, the data acquisition server 112 is programmed to producesuch information and convey it to the pulse sequence server 110. Forexample, during pre-scans, magnetic resonance data is acquired and usedto calibrate the pulse sequence performed by the pulse sequence server110. As another example, navigator signals may be acquired and used toadjust the operating parameters of the RF system 120 or the gradientsystem 118, or to control the view order in which k-space is sampled. Instill another example, the data acquisition server 112 may also beemployed to process magnetic resonance signals used to detect thearrival of a contrast agent in a magnetic resonance angiography (MRA)scan. By way of example, the data acquisition server 112 acquiresmagnetic resonance data and processes it in real-time to produceinformation that is used to control the scan.

The data processing server 114 receives magnetic resonance data from thedata acquisition server 112 and processes it in accordance withinstructions downloaded from the operator workstation 102. Suchprocessing may, for example, include one or more of the following:reconstructing two-dimensional or three-dimensional images by performinga Fourier transformation of raw k-space data; performing other imagereconstruction algorithms, such as iterative or backprojectionreconstruction algorithms; applying filters to raw k-space data or toreconstructed images; generating functional magnetic resonance images;calculating motion or flow images; and so on.

Images reconstructed by the data processing server 114 are conveyed backto the operator workstation 102 where they are stored. Real-time imagesare stored in a data base memory cache (not shown in FIG. 1), from whichthey may be output to operator display 112 or a display 136 that islocated near the magnet assembly 124 for use by attending physicians.Batch mode images or selected real time images are stored in a hostdatabase on disc storage 138. When such images have been reconstructedand transferred to storage, the data processing server 114 notifies thedata store server 116 on the operator workstation 102. The operatorworkstation 102 may be used by an operator to archive the images,produce films, or send the images via a network to other facilities.

The MRI system 200 may also include one or more networked workstations142. By way of example, a networked workstation 142 may include adisplay 144; one or more input devices 146, such as a keyboard andmouse; and a processor 148. The networked workstation 142 may be locatedwithin the same facility as the operator workstation 102, or in adifferent facility, such as a different healthcare institution orclinic.

The networked workstation 142, whether within the same facility or in adifferent facility as the operator workstation 102, may gain remoteaccess to the data processing server 114 or data store server 116 viathe communication system 117. Accordingly, multiple networkedworkstations 142 may have access to the data processing server 114 andthe data store server 116. In this manner, magnetic resonance data,reconstructed images, or other data may be exchanged between the dataprocessing server 114 or the data store server 116 and the networkedworkstations 142, such that the data or images may be remotely processedby a networked workstation 142. This data may be exchanged in anysuitable format, such as in accordance with the transmission controlprotocol (TCP), the internet protocol (IP), or other known or suitableprotocols.

Signals acquired using an MRI system 200, as described, unlike CT imagesignals, bear no direct relationship to electron density. Therefore, insome aspects, MRI system 200, independently or in cooperation with otherprocessing or analysis systems, may be configured to process magneticresonance data, reconstructed images, and/or other data to generatedata, images, or information suitable for use in radiation therapy, asdescribed.

Referring now to FIG. 3, a flowchart is shown setting forth steps of aprocess 300 for generating synthetic ED images, in accordance withaspects of the present disclosure. In particular, steps of process 300may be carried out using a number of suitable systems or devices,including imaging systems, planning systems, processing systems, and soon, or combinations thereof.

Specifically, process 300 may begin at process block 302 whereby aplurality of image data is received, for example from a data storage, orother computer readable medium, wherein the image data includes 1D, 2D,3D, and 4D image data. In some aspects, process block 302 may includedirecting a MRI system, as described, to acquire a plurality of imagedata for use in a radiotherapy process, and receiving the acquired imagedata therefrom. By way of example, received or acquired images, caninclude 3D fast low angle shot (FLASH) images using multiple flipangles, 3D balanced steady-state free precession (bSSFP) images usingmultiple flip angles, 3D GRE images using multiple echo times, 3D actualflip angle (AFI) images using multiple repetition times (TR's), and soforth. As described, acquired images may need to include the volumetriccoverage, slice thickness, spatial resolution, geometric fidelity, andimage uniformity suitable for use in radiotherapy process. In addition,with respect body regions susceptible to motion, appropriate acquisitionspeed and/or motion corrections may be needed for obtainingartifact-free images and hence accurate treatment plans. In accordancewith some aspects of the disclosure, a retrospective temporalreshuffling of k-space data may be utilized to generate phase resolved4D MR image data, as will be described.

At process block 304, a number of corrections may be applied to thereceived or acquired image data, as discussed below.

In particular, 3D gradient nonlinearity geometric distortion correctionsmay be applied to any or all images by applying various correctionalgorithms. In some preferred aspects, such corrections may be performedusing a vector deformation field (VDF) established by comparing reversedgradient images of a distortion phantom, whereby the phantom's designimages may be employed on a scanner-by-scanner basis. This approach ismore robust than the Legendre polynomial 3D distortion correctionalgorithm provided by the scanner manufacturer, after which residualgeometric distortions often remain.

Also, corrections may be performed to account for off-resonance effects,including main field (B0) inhomogeneity, chemical shift, and magneticsusceptibility effects. In particular, these may be corrected bygenerating magnetic field maps of the patient determined with phasedifference mapping based on 3D gradient-recalled echo (GRE) imagesobtained at two echo times. In some aspects, the GRE images used togenerate the magnetic field maps may be a subset of the multi-echo GREimages used for T2* mapping discussed below, thereby increasing scanningefficiency.

In addition, any or all images may be corrected for RF transmit/receivefield inhomogeneities. Specifically, rapid mapping of the B1+(RFtransmit field) may also be performed using the actual flip angleimaging (AFI) imaging method, which consists of a modified 3D FLASHsequence with two repetition times, large spoiler gradients, and aspecific RF phase cycling schedule. A 2nd to 7th order polynomialsurface may then be then fit to smooth the resulting flip angle (FA)maps, which are directly related to the B1+ field. In addition,correction for B1− (RF receive field) may also be performed usingphased-array coil sensitivities measured immediately prior to dataacquisition, for example, using a pre-scan.

At process block 314, a number of relaxometry maps are generated byassembling the corrected image data, including T1, T2, and T2* maps.Specifically, a rapid T1 mapping may be performed using a set of several3D FLASH images (e.g. 3 to 5) acquired with different flip angles, forexample, 2, 5, 10, 15, 25 degrees, although other values are possible.As described, images may be corrected for gradient nonlinearities andoff-resonance induced spatial distortions. A “flip angle series” may beformed and a T1 parameter map may be obtained by a least squares fittingof the flip angle series to the corrected flip angles, determined usingthe B1+ map. Also, rapid T2 mapping may also be performed using the setof several 3D balanced bSSFP images acquired with different flip angles.Each of the multiple flip angle images may be corrected for gradientnonlinearities and off-resonance induced spatial distortions. A flipangle series may be formed and a T2 parameter map may be obtained byleast squares fitting of the flip angle series to the corrected flipangles, determined using the B1+ map, as well as T1 and B0 parametermaps. Furthermore, T2* mapping may further be performed using acquired3D multi-echo GRE images, using, for example, a set of four to eightecho times, with an “echo series” of phase corrected real images beingoutputted. Echo times may be in the range of 0.07 to 30 ms, althoughother values are possible. As described, images may be corrected forgradient nonlinearities and off-resonance induced spatial distortions. AT2* parameter map may then be obtained by least squares fitting of thephase corrected real images to echo times.

A block diagram representing an example image acquisition andpost-processing steps, as described, is shown in FIG. 4. By way ofexample, illustrating the effects of B1+ inhomogeneities on T1 parametermaps, FIG. 5 compares corrected and uncorrected brain images from ahealthy volunteer. Without correction for B1+ inhomogeneity, T1 mapsshow considerable non-uniform image intensities (shading), asdemonstrated in the grayscale and color maps on the left of FIG. 5. Theshading translates into errors in the calculated T1 relaxometry values,which must be corrected for accurate tissue segmentations. Applying theB1+ correction results in highly uniform T1 parameter maps, as shown inthe grayscale and color maps on the right side of FIG. 5. Similarresults were obtained for T2 parameter maps.

Referring again to FIG. 3, at process block 308, relaxometry maps andother images generated based on corrected image data, as describedabove, may be segmented according to specific regions of interest(ROIs). For instance, a segmentation may be performed by thresholding MRrelaxometry parameters. In some aspects, the segmentation may beimplemented using Otsu's method, an approach that appliesclustering-based image thresholding, with T1, T2, and T2* values used asinputs. Other image segmentation approaches may also be used, automatedor semi-automated techniques.

As illustrated in the example of FIG. 21, MR scanners from variousmanufacturers produce images that can vary not only in homogeneity, asdescribed, but may also differ in overall intensities levels, asgenerally indicated by 2100. Although corrections for off-resonanceeffects, gradient inhomogeneity and other artifacts may be corrected asdescribed, such corrections do not affect the overall intensity levels,as shown by 2102. As such, in some aspects, images or maps may also becorrected for intensity variation by performing a normalizationprocedure. This may include receiving an indication of the scanner type,or manufacturer, and imaging conditions, such as magnetic fieldstrength, and applying a normalization in accordance with the receivedindication. As indicated by 2104, such normalization can achieve astandardization of images across manufacturers and imaging conditions,fixing window widths and levels in a manner that may be advantageouslyused during segmentation and position verification.

At process block 310, a tissue classification of ROIs segmented atprocess block 308 may be performed according to image intensitiesappearing the set of relaxometry maps. For example, an atlas-basedtissue classification may be utilized. Specifically, segmentedstructures or organs may be classified according published MRrelaxometry values (for example, T1, T2, and T2* relaxation times)acquired at a given magnetic field strength, such as, 3 Tesla.

Then, at process block 312, electron density values may be assigned tothe classified structures using an assignment process. In particular,structures or organs classified at process block 308 may be assignedtissue-specific electron density values. Such ED values may determined,or computed in any manner, or otherwise obtained from any reference,such as ICRU Report #46.

At process block 314, synthetic electron density image images, forexample, in the form of a 3D or 4D image set and corrected fordistortions and inhomogeneties, as described, may then be generatedusing the classified structures. In some aspects, corrected syntheticelectron density images may be converted to synthetic CT images, forexample, by utilizing an inverted CT-ED standard conversion table,generally available on clinical radiation treatment planning systems.FIG. 6 shows a graph illustrating a conversion between relative electrondensity and Hounsfield units (CT number).

Electron density values associated with the corrected synthetic ED or CTimages may allow any system, configured for radiotherapy plandevelopment or capable of performing dose calculations using electrondensity data, to generate treatment plans, as indicated by process block316. Such dose calculations help to determine a radiation dosedistribution, representative of dose received within, around, orgenerally about, any desired or target regions of interest. In someaspects, dose distributions may be then used or modified in an onlineradiotherapy plan adaptation strategy, as will be described.

By way of example, FIG. 7 displays synthetic ED (left) and synthetic CT(right) images of a pelvis for a healthy volunteer, generated accordingto the aforementioned description. Six tissue types were segmented andassigned electron densities: air, bone, fat, muscle, water (urine inbladder), and skin. Additional tissue classifications (for example,separating cortical bone from bone marrow) beyond that shown in FIG. 7may still be possible. The pelvic synthetic CT images displayed in FIG.7 were transferred and loaded onto a Monaco treatment planning system(Elekta, Sweden), where a volumetric modulated arc therapy (VMAT) planwas calculated with and without heterogeneity corrections turned on, asshown in FIG. 8. Differences visible between the solid and dashed linesin the DVH graph shown in the upper right, 802, of FIG. 8, as well asthe +/−90 cGy dose difference shown in the lower right, 804, of FIG. 8,demonstrate that the planning system utilized was able to use theinformation provided by the synthetic CT images to calculate dose withheterogeneity corrections turned on.

The above-described process demonstrates a model-independent,MRI-scanner-independent methodology for synthesizing electron densityinformation from MR image data suitable for use in radiation therapyplanning and delivery. The relaxometry parameter maps from whichsynthetic electron density and synthetic CT images may be derived, arefully corrected for known sources of spatial distortion, non-uniformimage intensities and variations in grayscale signal intensities,commonly experienced in conventional magnitude MR images. These featuresin particular resolve current roadblocks precluding the routine use ofconventional magnitude MR images in MR-based radiotherapy and adaptiveradiotherapy applications. In addition, the acquisition of theunderlying images, from which the synthetic electron density andsynthetic CT images are obtained, are fast enough to be performed inbreath holds, permitting generation of synthetic CT images in thechallenging body regions prone to motion artifacts. Although thedescribed approach does not rely on anatomical models (atlases) ordeformable registration, these could be used in conjunction with systemsand methods of the present disclosure, if so desired.

As may be appreciated, knowledge of target and organ at risk motiontrajectories is critical in radiation therapy. Motion can result in asmearing of planned dose distributions, particularly when steep dosegradients are employed to reduce doses to proximal OARs. A number ofstrategies have been introduced to manage motion. However, in general,these methods suffer from one or more of the following deficiencies: useof motion surrogates (e.g., bellows, reflector camera, body surfacearea), invasive implantation of RF transponders, use of ionizingradiation, poor soft tissue contrast, finite penetration depth, lack oftemporally correlated spatial information, large irradiated volumesresulting from large margins, diminished treatment efficiency resultingfrom gating.

Due to its non-ionizing and high soft tissue contrast properties, MRimaging represents an ideal four-dimensional (4D) imaging platform.However, spatio-temporal-contrast resolution tradeoffs precludeacquisition of true, 4D MR images. For instance, the mean, humanrespiratory period is approximately five seconds. In order to resolvemotion similarly to 4D-CT (in which the respiratory cycle is decimatedinto 10 phases), the ideal 4D-MRI method would need to acquire anartifact-free, high-contrast, high-resolution 3D volume every 0.5seconds or less. Even with the latest MRI technology, including parallelimaging, multiband excitation, and compressed sensing, this requirementis still not achievable. Furthermore, the introduction of MR-guided RTplaces additional demands on fast, volumetric MR imaging. Exceptiongating and real-time tumor tracking necessitate low latencies in theimage acquisition, reconstruction, and segmentation chain.

Therefore, in accordance with aspects of the present disclosure, true4D-MR images for use in RT treatment may be obtained by performing aretrospective temporal reshuffling of k-space data acquired whilepatients breathe normally. This approach does not rely on externalrespiratory surrogates (bellows, reflector camera, body surface area,etc), improves image contrast, and eliminates imaging radiation dose.

By way of example, a data acquisition is described herein. Inparticular, a golden angle radial pulse sequence may be utilized capableof switching between 2D and 3D cine modes of either balanced steadystate free precession (bSSFP) or spoiled gradient recalled echo (SPGR)acquisitions. The DC signal from each acquired radial spoke may be usedas a navigator, providing a respiratory surrogate to guidephase-resolved image reconstruction. De-rated maximum gradientamplitudes and slew rates may be used to minimize eddy current effects.

Coronal 4D-MR images, for example may be collected with the sequencerunning in 3D cine mode with the following parameters: FOV=380 cm,TE=0.8 msec and TR=1.9 msec, As described, a total scan time can beabout six minutes. Although, a particular scan implementation has beendescribed above, one skilled in the art will appreciate that variousmodifications to scan parameters may be performed and considered withinthe scope of the present disclosure.

Images based on acquired data, as described, may then be reconstructedusing the method illustrated in FIG. 22. In some aspects, raw k-spacedata may be transferred and processed offline any systems suitable. TheDC navigator signal for each radial spoke (or phase encode line) may bedetermined, and plotted over time, as shown in FIG. 22. A lookup tableof bin start times may be generated by decimating each respiratorycycle, obtained from the navigator waveform, into say 10 temporal bins(phases), based either on amplitude or phase. The lookup table of binstart times may then be used to reshuffle the raw 4D-MRI k-space intosay a 10-bin (10 phase) hybrid k-space, using the time stamps in theheaders of each acquired radial spoke (or phase encode line) orknowledge of the pulse sequence looping structure. Spokes or linesreshuffled to the same location in hybrid space may then be linearlycombined, with weighting based on temporal proximity to the center ofthe bin. Missing lines following the reshuffling may be filled usingcompressed sensing reconstruction. A 3D FFT may then be applied toconvert the reshuffled hybrid k-space data to images for each bin (e.g.,0%, 10%, 20%, etc). The final images for each temporal phase may then beinterpolated to a resolution of say 1 to 2 mm³ and converted to DICOMfor subsequent image processing. As described, such images may beutilized to obtain synthetic ED or CT images at each phase of therespiratory cycle.

The above acquisition describes a switchable spoiled or balancedsequence. This facilitates certain tumors being better visualized withT1 contrast, as opposed to mixed T2/T1 contrast. Furthermore, for tumorsand tissue with slow kinetics (e.g., washout), target visualization onthe 4D data can be improved by acquiring the 4D data post-gadolinium.For example, visualization of hepatocellular carcinoma (HCC) andesophageal cancer would benefit from T2/T1 contrast obtained using thebSSFP sequence. However, visualization of liver metastasis would benefitfrom T1 contrast obtained using the SPGR sequence at a delay followingcontrast administration. Visualization of lung lesions would benefitfrom SPGR acquisition through reduced banding artifacts.

In addition, acquiring a DC navigator during 2D cine imaging is a novelapproach, which combines the advantages of pencil beam navigators andrapid cine imaging. Similar to pencil beam navigators, extremely lowlatency are acquired (image reconstruction is not required). Thisprovides information from a pencil beam navigator simultaneously withimaging. Also, the DC navigators are obtained with each phase encode anddo not require image reconstruction. This has advantages for MRI-gRT, inthat the DC navigator, acquired for each phase encode line, hasextremely low latency, and could be used in real-time tumor trackingprediction algorithms to drive the system of multi-leaf collimators(MLCs).

In some aspects, a motion analysis may be performed using imagesobtained as described above. For instance, respiratory phase images ofeach 4D-MRI dataset may be analyzed to determine a motion. For example,gross target volumes (GTV)s may be contoured on the first temporal phaseand then propagated to the other nine temporal phases using a deformableimage registration process. The procedure may also be repeated for otherstructures or organs, organs at risk, and so on. Using the center ofmass (COM) components along each Cartesian axis for each of thecontoured structures a principal component analysis (PCA) may beperformed to determine the eigenvectors and eigenvalues of GTV andstructure COM motion. Such motion analysis may be useful forimplementation during RT treatment delivery, as will be described.

Synthetic CT images generated using systems and methods, as described,exhibit several advantageous features for MRI-based radiation therapycompared to other alternatives, including, full compatibility withexisting kilovoltage (kV) and megavoltage (MV) CT-based IGRT techniques,full compatibility with rapidly developing MRI-based IGRT technologies,and full compatibility with MRI or CT-based adaptive radiotherapy, thataccount for daily changes in the position, size, and shape of tumor andcritical structures, and permit additional reduction of margin size.Additionally, in some envisioned aspects, application of theabove-described methodology may be extended to proton-based radiationtreatment planning and delivery methods, as well as, systems andapplications requiring attenuation correction of positron emissiontomography (PET) images, such as combined PET/MRI scanners.

Turning back to FIG. 1, at process block 12 any multi-modality imageinformation acquired, such as described above, or in any other way, maybe processed or analyzed using any desired systems and methods to yieldimage information for use in treatment plan generation and delivery. Aswill be described, post-acquisition processing of images may involve anynumber of steps or approaches. For example, these can be providingcorrections for known sources of noise, or applying various imagetransformation, deformation, or registration algorithms, or determiningoptimal display characteristics to facilitate accurate identification ofanatomical structures (for example, widow or level), or delineatingobjects of interest, or generating synthetic images, to name but a few.

Many commercial systems in use today are capable of generatingmulti-modality image data captured contemporaneously, such as usingCT/MRI, PET/CT or PET/MRI systems. However, in many cases, such systemsare either not available or not part of the clinical workflow practicefor radiation therapy. Thus, typically, at process block 12 separatelyacquired multi-modality images may need to be combined to providecomplementary information requisite for accurate determination ofcritical objects. Since typically such images are acquired usingdifferent scanners, and usually at different times, transformation, orregistration, as is known in the art, is required for images orcontoured structures in different sets of data to occupy the samecoordinate system. Simply, the process of image registration involvesdetermining a geometrical transformation that aligns points in one viewof an object with corresponding points in another view of that object oranother object, where the view may be a two- or three-dimensional view,or the physical arrangement of an object in space.

Image registration typically involves inter-modality images, such as CTand MR images, MRI and PET images, PET and CT images, contrast-enhancedCT images and non-contrast-enhanced CT images, ultrasound and CT images,and so on. In addition, image registration may also be used forintra-modality imaging, wherein structures may be shifted or distortedbetween image sequences acquired at different times. FIG. 9 illustratesan example of a registration between a fixed image 902 and moving image904. Therefore, in the context of adaptive radiotherapy, it maydesirable to adjust or modify images or contours of targets and/orcritical structures, identified and employed at an initial radiationtherapy planning stage, according to daily or current imaging, whosevolumes or shapes may have changed above a desired threshold.

Among the many free form registration approaches, the parameterizedb-spline deformable image registration (DIR) method has the benefit ofautomation efficacy, local deformation control and multi-modalitycapability. However, the deformation pattern of the model does notnecessarily follow the real movement of the organs due to lack ofphysical model support, and therefore induces artifacts such as bonewarping and inaccurate fine structure correspondence. Thus, alternativeimage registration approaches, that are more accurate, are desirable.

In one aspect of the present disclosure, a new registration method isprovided, designed to mitigate the drawbacks of previous methods. Inparticular, a modified parameterized b-spline deformation model may beutilized, designed to overcome previous limitations by using a newregularization method, namely bending energy limited diffeomorphism(BELD). Diffeomorphism regularization is typically used to guarantee thedeformation smoothness, and is computationally costly due to Jacobianmatrix calculation of all grid points in every iteration step. However,diffeomorphism regularized result does not necessarily follow the realdeformation path. Thus, in the present disclosure, accuratediffeomorphism is achieved by limiting the difference between adjacentgrid deformation parameters, instead of computing heavily the Jacobeanmatrix as is common in traditional approaches. The bending energypenalty may be further used to limit the freedom of smoothness,preserving structure topology, and increasing the registration accuracy.Thus, the bending energy penalty in different parts of the images canthen be differentiated conveniently through auto-segmentation of majorcomponents (bone, body liquid, tissue and air) of both target and sourceimages.

In particular, the bending energy may be used as a penalty term inB-spline deformation mode, calculated as follows:

$\begin{matrix}{{E_{B} = {{{\sum_{\overset{\rightharpoonup}{r}}\left( \frac{\partial^{2}T_{\overset{\rightharpoonup}{r}}}{\partial x^{2}} \right)^{2}} + \left( \frac{\partial^{2}T_{\overset{\rightharpoonup}{r}}}{\partial y^{2}} \right)^{2} + \left( \frac{\partial^{2}T_{\overset{\rightharpoonup}{r}}}{\partial z^{2}} \right)^{2} + {2\left\lbrack {\left( \frac{\partial^{2}T_{\overset{\rightharpoonup}{r}}}{{\partial x}{\partial y}} \right)^{2} + \left( \frac{\partial^{2}T_{\overset{\rightharpoonup}{r}}}{{\partial y}{\partial z}} \right)^{2} + \left( \frac{\partial^{2}T_{\overset{\rightharpoonup}{r}}}{{\partial z}{\partial x}} \right)^{2}} \right\rbrack}} = 0}},} & (3)\end{matrix}$

where T(

) is the local transformation at location

. This term is computationally costly when soft tissue makes up themajority part in the region of interest. If the B-spline transformationis written in the tensor product form

$\begin{matrix}{{d^{l}\left( \overset{\rightarrow}{r} \right)} = {\sum_{i,j,k}{c_{i,j,k}^{l}{\beta^{n}\left( {\frac{x}{m_{x}} - i} \right)}{\beta^{n}\left( {\frac{y}{m_{y}} - j} \right)}{\beta^{n}\left( {\frac{z}{m_{z}} - k} \right)}}}} & (4)\end{matrix}$

where l∈{x, y, z} and β^(n) is a nth order B-spline basis, the penaltyfunction from is defined as

$\begin{matrix}{{p(t)} = \left\{ \begin{matrix}{{\frac{1}{2}\left( {t + \zeta_{1}} \right)^{2}},} & {t \leq {- \zeta_{1}}} \\{0,} & {{- \zeta_{1}} < t \leq {- \zeta_{2}}} \\{{\frac{1}{2}\left( {t - \zeta_{2}} \right)^{2}},} & {otherwise}\end{matrix} \right.} & (5)\end{matrix}$

where the argument t denotes a difference between two adjacentdeformation coefficients. When combined with B-spline coefficient, thepenalty function is R(c)

R(c)=Σ_(l∈{x,y,z})Σ_(i,j,k) {p _(x) ^(l)(c _(i+1,j,k) ^(l) −c _(i,j,k)^(l))+p _(y) ^(l)(c _(i,j+1,k) ^(l) −c _(i,j,k) ^(l))+p _(z) ^(l)(c_(i,j,k+1) ^(l) −c _(i,j,k) ^(l)),  (6)

where ζ₁=0 and ζ₂=0 would correspond to the volume preserving constraintdet J_(T)(

)=1 for any

.

Diffeomorphism can be guaranteed using the above method, but within thisconstraint the deformation still has infinite freedom, while a bettersolution would satisfy a minimized bending energy requirement. In theBELD approach, instead of building sophisticated reality models, asimplified semi-physical model may be used, where diffeomorphism islimited by minimizing the bending energy at grid points, which is muchcomputationally lighter. With regularization from rigidity map M(

), BELD constrain and feature point distance, the overall penaltyfunction becomes

γ₁ M(

)ƒ(

)+γ₂ R(c)+γ₃Σ_(m≠n)(

_(m)−

_(n))²+γ₄ E _(B)  (7)

where

_(m) and

_(n) are physical coordinates of corresponding feature points, γ_(i) areempirically determined weighting factors. Although the four extrapenalty terms may appear to increase the computational complexity, eachterm is effective only in limited regions, and hence can be optimized inconsecutive stages. Also, due to the nonlinearity nature of the penaltyfunction and mutual information metric, a nonlinear conjugated gradientoptimization method can be utilized. The search direction may be definedas a linear combination of the cost function gradient and the previoussearch direction.

In some configurations, the deformable registration method, asdescribed, may be implemented as a software tool, either integrated orin conjunction with any radiation planning systems, image analysis orprocessing systems and software. Such tool may include a variety ofsteps and functionalities, such as, for example, the capability to (1)accept input of images of different modalities, (2) convert existingcontours on any reference images (e.g., MRI) into delineated volumes andadjust image intensity within volumes to match target images (e.g., CT)intensity distribution for enhanced similarity metric, (3) registerreference and target images using an appropriate deformable registrationalgorithms, as described, (e.g., b-spline, Demons) and generate deformedcontours, (4) map deformed volumes on target images, calculate mean,variance, and center of mass as the initialization parameters forconsecutive fuzzy connectedness (FC) image segmentation on target image,(5) generate affinity map from FC, (6) generate contours by modifyingthe deformed contours using the affinity map with a gradient distanceweighting algorithm. In addition, such software tool may benefit fromGPU processing, which may be orders of magnitude faster than using CPUprocessing, on account of a highly parallel and efficient processingstructure.

This approach, was tested in a variety of clinical cases. FIG. 10 showsan example of BELD-regularized deformable image registration applied toa breast cancer case, with the patient in supine position. From left toright, images in panel (A) show the original MR and CT images,respectively, where the CT image contains contours structures. Then, theimage on the left of panel (B) shows contours transferred from the CTusing a DIR with conventional un-regularized method, along with therespective transformation field on the left of panel (C). By contrast,images on the right panels of (B) and (C) show the transferred contoursand deformation fields, respectively, using the BELD regularizedapproach, as described. FIG. 10 demonstrates the improvement of usingBELD regularization over the conventional (un-regularized) method for abreast cancer case with patient in the supine position. It is clear thatthe un-regularized DIR results in an unrealistic deformation field withreduced registration accuracy. By contrast, both deformation field andregistration accuracy are improved with the BELD regularization of thepresent disclosure.

FIG. 11 shows an example of BELD-regularized DIR for a breast cancercase with a patient in prone position. The top and bottom images showoriginal MR (top) and CT (bottom) images, respectively. Panel (A) showsbreast and lumpectomy cavity contours from the MR image overlaid ontothe CT (bottom) image using a rigid registration algorithm. By contrast,the top image of panel (B) shows contours transferred from CT to MR,while the bottom image of panel of (B) shows the transferred contoursfrom MR to CT, based on the BELD-regularized DIR. Blue contours aredeformed from the red contours on the original MR images. Comparing toun-regularized registration, which resulted in a difference between theactual and transformed contours up to 10 mm (average of 4.2 mm), theBELD-regularized DIR reduced the average difference to 1.8 mm. Thisdemonstrates that the present disclosure allows for contours of breastand lumpectomy cavity to be accurately transformed from MR to CT forusing in radiation treatment planning.

In addition, this approach was also tested for CT and MR images ofpancreatic cancer patients acquired at the same respiration phase tominimize motion distortion. Dice's coefficient was calculated againstdirect delineation on a target image. Contours were generated by variousmethods, including rigid transfer, auto-segmentation, deformable onlytransfer and regularized b-spline method, as described above, werecompared. Although fuzzy connected image segmentation involved carefulparameter initialization and user involvement, automatic contourtransfer by multi-modality deformable registration, as described,provided up to 10% of accuracy improvement over the rigid transfer.Providing two additional steps of adjusting intensity distribution andmodifying the deformed contour with affinity map may furtheradvantageous improvement up to 14% in transfer accuracy.

FIG. 12 shows an example of results from multi-modality contouredstructures treated by deformation fields in accordance with the presentdisclosure. Specifically, PET, and various MRI including T1, T2, DWI andDCE, were rigidly registered with CT images for representative patientswith pancreatic cancer. Gross tumor volume (GTV) and organs at risk(OARs) were delineated on the multi-modality images and were processedusing a deformable multi-modality image registration tool, as described.Substantial variations among contours from multi-modality images wereobserved for both GTV and OARs, as illustrated in FIG. 12. The upper twoimages 1202 of FIG. 12 show a large variation in volume from severalstructures among the different overlaid contours generated usingmulti-modality images. Derived deformation fields were then applied todeform the corresponding modality contours, which were subsequentlyoverlapped onto the planning CT. The lower two images 1204 of FIG. 12show increased overlapping among different modality contours aftertreated using deformation fields. When using T1 weighted contour volumeas nominator, the delineated volumes vary from 0.5 to 1.8 for GTV, from0.81 to 1.05 for OARs between the image modalities. The overlappingratio between different modalities varies from 0.22 to 0.74 for GTV, andfrom 0.65 to 0.84 for OARs. After deformable image registration, thecontour volumes were changed by deformation fields by 6% to 11%. Theremaining variations after DIR observed in FIG. 12 were mostly due tothe inherent difference between the imaging modalities. These changes donot impact volume variation between different modalities, but improvesignificantly the overlapping ratio between contours from differentmodalities, for example, changing from 0.55 to 0.82 for GTV. Therefore,deformable image registration of multi-modality images increases theagreement between contours from different image modalities, improvingaccuracy for target and normal structure delineation for radiationtreatment planning of pancreatic cancer.

Registration algorithms often make use of a smoothing kernel to reducediscontinuities in the deformation field applied between iterations. Inanother aspect of the present disclosure, a novel method is provided forimplementation in systems and methods configured to perform imageregistration, wherein the dimensions of the Gaussian smoothing kernel isadaptively modified during runtime according to a threshold, such as arate of convergence for the deformable image registration. Inparticular, a large kernel size may be used initially and then reducedthrough subsequent iterations. With other approaches, as the iterationnumber increases, the rate of convergence begins to decrease until anasymptotic value is reached. At this point, the accuracy of theregistration remains unaffected by further iterations. By contrast, inthe present disclosure, varying the kernel size allows for asymptoticconvergence to be avoided and the accuracy may then be able to continuetoward a new range of possible values. In this manner, as the size ofthe smoothing kernel is stepped down, the accuracy continues to improve.This approach has several advantages benefiting improved registrationaccuracy over traditional methods employing demons-based registrationalgorithms. Specifically, one advantage is that the kernel is robustenough to handle large deformations, such as those found in the bladderand rectum, yet sensitive enough to handle fine details, and also istypically immune to non-physical warping. Additionally, it also providesfor a spatial alignment between common structures in the images, makingfurther registration easier.

FIG. 13 shows an example demonstrating the effect of a variable-kernelsmoothing technique on registration accuracy. Typically, the accuracyimproves after a number subsequent iterations, as indicated by adecrease in the average pixel difference between the images. However,when only using a given kernel size, the accuracy begins to converge toa constant value (dashed lines). As such, by adjusting the size of thekernel adaptively (circled regions) when a specified condition is met,pre-mature asymptotic convergence can be avoided, and the algorithmallows for transition to a new range of possible values. This leads toimproved registration accuracy, exceeding the limits possible with aconstant kernel size.

Such approach allows for fast and accurate deformable image registrationof cone beam CT (CBCT) and CT images, typically employed in onlineadaptive radiotherapy. Tests showed that a variable-kernel methodresulted in improved accuracy, beyond that for a constant kernel size.Specifically, the planning CT and daily CBCT acquired for 6 prostatecancer patients were registered using the above-described technique.Histogram matching was used to compensate for intensity differencesbetween the two modalities. The Pearson correlation coefficient (PCC)and volume overlap index (VOI) were used to quantify registrationaccuracy. Results showed that the iterative decrease of the smoothingkernel size allowed the algorithm to converge to an increasinglyaccurate solution, beyond the asymptotic limit for a constant kernelsize. The mean VOI was calculated for bladder, prostate, and rectum withvalues of 91.9%, 68.7%, and 78.2%, respectively. The correlationcoefficient was calculated for every fraction in the overlapping CT andCBCT scan volumes in each patient data set. Average PCC values were0.9987, 0.9985, 0.9982, 0.9980, 0.9985, and 0.9985 for the six patients.A typical runtime for a 512×512×70 image volume was 4.6 minutes.Therefore, results using DIR technique of the present disclosurevalidates its use for deformable CT-CBCT registration, eliminating theneed for multi-resolution processing and continual up-sampling of theimage, which can be computationally intensive.

To illustrate the robustness of the described algorithm in processinglarge deformations, as seen, for example, in typical prostate cancercases, FIG. 14 shows registration results for a pelvic CT scan.Specifically, a planning CT (a) was deformably registered to a daily kVcone beam CT (c) using the described technique. The resulting image (b)was in excellent agreement with the CBCT image. The included plotdisplays correlation coefficients for the entire image volume (70slices) before and after registration, demonstrating the consistency ofthe method. This demonstrates the technique's ability to processrelatively large deformations, with particular with reference to therectal area, where the algorithm was able to compensate for considerablewarping of the surrounding tissue. Noticeable differences exist betweenthe preregistered images, after registration, the resulting images agreevery well.

Additionally, intra-modality DIR techniques make use of the Demonsalgorithm to provide intensity mapping for the conversion of severalmodalities to CT-like contrast scales. These may operate on varyingtypes of CT (e.g., cone beam CT, MVCT), as well as ultrasound (US)images. In particular for US, such approaches may implement acorrespondence function designed specifically for ultrasound. However, asimple relationship between tissue echogenicity and CT Hounsfield unitshas yet to be established. As such, current US-CT registration practiceshave been limited to rigid registration or the use of inter-modalitymethods, such as thin plate splines, which require manual pointselection.

High-frequency ultrasound (HFUS) images are capable of revealingaccurate spatial information, for example, in skin lesions, and may beused to plan and guide radiation therapy (RT) for skin cancer.Therefore, in yet another aspect of the present disclosure, systems andmethods are provided for performing fully-automated, accurate deformableregistration between US and CT images, which may be beneficial for skincancer patients requiring radiotherapy treatments. Specifically, a novelDIR technique is introduced, based on the symmetric force Demonsalgorithm, in which the size of the Gaussian smoothing kernel may beadaptively adjusted. A correspondence function may also be used to mapattenuation values to tumor elasticity and a histogram matching mayimplemented. The Pearson correlation coefficient (PCC) may be used as anindicator for assessing registration accuracy.

This approach provides robustness to the algorithm, as described, underthe large deformations, typically encountered during skin tumorshrinkage. Since skin lesions, in particular, have the added benefit ofa clear external boundary, the present disclosure also facilitates useof an anisotropic diffusion filter to perform edge-preserving smoothing,thereby reducing noise and increasing registration accuracy. Suchadvantages may be particularly important for superficial US images,which can suffer from poor image quality and low contrast. Additionally,the intensity matching technique typically used to convert betweendifferent CT image types, may also be applied to US images acquired atdifferent frequencies, making US-US registration possible.

To demonstrate the feasibility of this approach, HFUS images of skinlesions and their corresponding CT images were registered for selectedregions of interest (ROI), in accordance with the present disclosure.FIG. 15 shows an example illustrating a registration between CT and HFUSfor a patient with a superficial basal cell carcinoma (BCC) in thethigh. The figure shows the CT (a) and high frequency ultrasound (b)image of the lesion, while c) and d) are enlarged views of therectangular regions in a) and b). e) and f) are the histogram-mapped andthe contrasted-enhanced HFUS images, respectively, and g) and h) areregistered images of CT and HFUS using the above-described variablekernel smoothing technique. PCC values of 0.9794 and 0.9815 (enhancedcontrast) were observed, indicating excellent agreement between thedynamic (HFUS) and static (CT) images. The registration technique alsoexhibited an ability to process large deformations, such as ROIdisplacement exceeding 200% of tumor thickness observed near lesionedges. The gradual decrease of kernel dimension was found to preventnon-physical warping, thereby improving registration accuracy. Thisrobustness is critical to skin cancer RT as tumors may shrinksignificantly during treatment.

Therefore, combined with standard image processing techniques, themethod of the present disclosure is sufficiently accurate to achievedeformable registration of HFUS and CT images for skin cancer RT. Thismay be advantageous in the ability to perform image-guided treatment forskin cancer, which could reduce geographic missing and may spare morehealthy tissue. The presented technique is accurate and robust enough toprocess large deformations, making it a promising tool for RT planningand delivery guidance for skin cancer.

Software tools for use in multiple-modality image registration, asdescribed, may achieve physically accurate registration with minimizeduser interference and computation cost, making multi-modality deformableimaging registration fast and accurate, in particular within the contextof ART re-planning or other real-time applications. Specifically, theapproach of the present disclosure may improve the accuracy of contourtransfer between different image modalities under challenging conditionsof low image contrast and large image deformation, in contrast to commonmethods utilized in radiation treatment planning.

Returning to FIG. 1, at process block 14 processed multi-modalityimages, as described, may then be received, for example, from an imagingsystem or a data storage, and transferred to a planning system for usein generating a radiotherapy plan. Specifically, in the case that a planis formulated for a first treatment, this process step typicallyincludes the determination of beam or radiation source deliverytechniques and arrangements based upon selected or determined planningaims. In certain planning approaches, this may include generating beam'seye-view displays, designing field shapes (blocks, multi-leafcollimators), determining beam modifiers (compensators, wedges) anddetermining beam or source weightings. Using contoured criticalstructures, performed either manually or using an automatic contouringtool, dose calculations may then performed based on selected algorithmsor methodologies. Using set relative and absolute dose normalizationsand dose prescriptions, a plan quality is then evaluated based on visualcoverage comparisons, dose volume histogram analysis, and tumor controland normal tissue complication probabilities. Automated orsemi-automated optimization tools may then allow for plan improvementbased on the planning aims and tolerances.

In some cases, generated plans may also involve taking into accountintra-fractional motion, such as respiration motion, of target andcritical structures. Thus, in accordance with some aspects of thedisclosure, a 4D planning approach for intra-fractional motion based on4DCT or 4D MR images partitioned into, for instance, 5 phases, althoughother values are possible, includes the following aspects: (i) creatingan IMRT or VMAT plan for one specific breathing phase (e.g, end ofinhale) based on the phase image by a full scale optimization, (ii)populating this plan onto the other remaining breathing phases byapplying segment aperture morphing (SAM) algorithm to take into accountchanges of the anatomy between breathing phases, and (iii) combining allphase-specific plans to form a 4D plan. Essentially, all phase planshave the same number of segments, with each segment having severaldifferent MLC patterns, dependent upon the number of phases, with thesame MU, jaw settings, gantry angle, etc. The 4D plan generated in thisway may be delivered using dynamic MLC and adjustable dose rate suchthat each segment can be delivered within an integer number of breathingcycles, ensuring that each MLC pattern is delivered with itscorresponding breathing phase. In particular, the delivery file from themulti-leaf sequencer is generated as a function of breathing period. Ateach treatment fraction, the delivery file can be quickly updated withthe breathing period obtained immediately prior to the delivery. Ifbreathing cycle changes during the delivery, the delivery file can alsobe updated with the necessary frequency during the delivery.

For situations when respiration is substantially different as comparedto when the planning images was acquired, an online adaptive 4D planningand delivery may be implemented in the following steps: (1) a referenceplan is generated based on a single-phase image (reference phase, e.g.,end of inhalation) from the planning image sets; (2) a comprehensivedry-run QA will be performed for the reference plan; (3) at the time ofa treatment fraction, the reference plan is modified using the SAMalgorithms based on the anatomy change on the reference phase image ofthe 4D images acquired with patient in the treatment positionimmediately prior to the treatment delivery; (4) the newly createdreference plan (adaptive plan) is populated based on each of theremaining phase images of the day using the SAM algorithms; (5) allphase plans are then sequenced taking into consideration the mostcurrent breathing information; and (6) the 4D plan is delivered underreal-time image guidance (e.g., orthogonal cine MRI) and its deliverymay be interrupted if MLC fails to track the target due to the abruptchanges in patient positioning or respiration motion.

Changes to targets and critical structures between treatment fractions,or over the course of several fractions, may produce significantdeviations from the original plan, with potentially negative impacts intreatment efficiency. As such, providing a radiotherapy plan at processblock 14 may, in some cases, involve performing applying techniques foradaptation of an original or first plan, such as those afforded by ART,to account for patient-specific anatomic and/or biological changesduring the course of treatment, using any combination of online andoffline approaches, as will be described.

Traditional plan optimization approaches are not typically fullyautomated, since often the optimization algorithm delivers undesirableresults, requiring additional iterations with different weightedobjectives. This is due to the difficulty in estimating how much can beachieved for different planning objectives, commonly defined in terms ofdose-volume constraints. For instance, some objectives may be formulatedas portion of an organ or structure not being allowed to exceed a dose.As an example, one such objective can be that 30% of a rectal volumecannot receive a radiation dose of more than 60 Gy.

If an individual OAR's objective is too stringent, as when a dose-volumeis placed too low, or a relative weight of the goal is too high comparedto other objectives, this may create undesirable effects, such asinsufficient tumor coverage or radiation dose hotspots. Since volumes ofOARs change as often as day-to-day, it may not possible to know exactlyhow much OAR radiation sparing is possible, or specifically what minimumpercentage of volume needs to be irradiated for certain amounts ofdoses. Therefore, the iterative process of finding the right objectiveweighting is tedious and time-consuming, requiring expertise andexperience.

Previously proposed technologies aiming to overcome this obstacleattempted either to determine the best achievable dose volume histogram(DVH) by computing OAR/target overlapping fractions or utilizedmulti-parameter criteria optimization, where each objective isseparately optimized and a linear interpolation is then performed todetermine an appropriate compromise. In particular, the multi-criteriaoptimization method is impractical for online ART since it requiresseveral optimizations. In addition, both methods are designed forinitial plan optimization, not re-planning, and require whole set ofOARs to be generated.

Contours obtained by organ delineation are a time consuming part of anyonline adaptive re-planning, with OARs typically representing themajority contours to be delineated. Specifically, organs around thedigestive tract, such as liver, rectum, bowels, and stomach are veryproblematic, since they are large organs and require many contours. Inaddition, their size, shape and content changes drastically andunpredictably from day to day, which makes it very hard for theauto-contouring methods to accurately generate contours, typicallyrequiring human delineation/editing.

As described, fast online re-planning methods require efficientapproaches to modifying radiotherapy plans, often while a patient lieson the treatment table. To overcome the drawbacks of previous onlinere-planning methods, in another aspect of the present disclosure,systems and methods are provided that make use of a novelGradient-Maintenance (GM) algorithm that allows for fully automatedonline ART re-planning without the need of OAR contouring.

As will be described, the GM algorithm calls for creating or adjustingbeam or segment apertures based on a target of the day, and optimizingbeam or segment weights using either ring structures generatedautonomously or semi-autonomously or isodose contours automaticallyconverted from isodose lines on, for example, an image of the day. Thealgorithm determines dose gradients from the dose distributions in theoriginal plan for each of the critical structures in the vicinity of thetarget, and initiates a re-planning optimization procedure aiming tomaintain the originally planned dose gradients. The algorithm canenhance automation, drastically reduce planning time, and improveconsistency throughput of online re-planning.

Specifically, unlike methods that try to determine, in a time-consumingfashion, “what is achievable” such that optimization goals may be set,the approach of the present disclosure efficiently determines dosegradients, providing a direct measure of “what can be achieved,” imposedby physical dose deposition limitations. In this manner, transfer ofdose gradients from the original to the modified plan may result in thebest plan achievable with respect to allowed physical dose depositionconstraints. Such approach affords several advantages over previoustechnologies. Specifically, generation of organs at risk (OAR), forexample, on a daily basis may not required, thus reducing delineationpractice to only the treatment target. In addition, traditionaloptimization algorithms strive to satisfy a group of objectives,specified in terms of dose volume constraints for each of the OAR, whichusually requires volumes to be generated, for example, on daily imagesets. By contrast, the approach of the present disclosure strive toarrive at certain dose gradients from the surface of the target towardeach organ at risk, allowing the optimization to be more reproducibleand predictable, and less likely to require multiple trial and erroriterations. In this manner, human involvement may be reduced oreliminated along with and time cost of optimization, which is anattractive feature for fast online re-planning.

The GM algorithm, as described, may be configured to work withindedicated optimization software and hardware, or may be combined alongwith systems configured for fixed- or rotational-beam radiationdelivery, or combinations thereof. In some envisioned implementations,maintaining dose gradients from the surface of the target, as described,may be achieved by having a planner communicate desired planning goalsto an optimization algorithm via a weighted sum of goals called“objective function,” which, in some aspects, may be generated using thedose gradients. Since many commercially available optimization systemsonly permit objective functions to be defined in volumes of interest anddose-volume goals, the approach of the present disclosure may be adaptedto allow for user-generated partial concentric ring structures (PCR) tobe employed in defining the desired dose gradients for the optimizationalgorithm. As such, any desirable system or methods may be used togenerate PCR. For example, PCRs may be generated using manual orauto-contouring approaches. Anatomical sites that would benefit mostfrom the approach of the present disclosure could be prostate andpancreas, given the large number of surrounding OAR structures, withtypically large daily unpredictable volume changes and relatively smalltarget organ sizes. Example PCRs 2300 are illustrated in FIG. 23 for aprostate cancer case.

Turning to FIG. 16, an example online plan adaptation process 1600 isshown, illustrating a mode of operation for the GM algorithm. Inparticular, such process 1600 may be utilized when any condition hasbeen met that would trigger a plan modification, such as, for example inthe case of reduced target coverage or increased radiation dosehotspots. The process 1600 begins at process block 1602, where aradiotherapy treatment plan is provided as input to any systemsconfigured to carry out the process 1600, such as a treatment planningor delivery station. The treatment plan input, defined at least by aradiation dose distribution, may be a plan generated at the onset oftreatment schedule, or may be a subsequently modified plan. In someaspects, a plan may be generated at process block 1602 usingconventional planning techniques, such as IMRT optimization. Such plancan achieve the desired dose-volume objectives for both targets and OARsusing full sets of contours on planning images, and can be a bestpossible plan for the planning technique utilized, given that theplanner is not necessarily under a time constraint.

At process block 1604, using the radiation dose distribution from theprovided or generated plan, dose gradients are determined defining avariation in radiation dose from at least one target structure towardany or all of the non-target structures or objects at risk. Such dosegradients may be used in defining any number of optimization objectives,which may be desirable in an online plan optimization process, asdescribed. In particular, radiation dose distributions in treatmentplans already subjected to an optimization may potentially represent anoptimally achievable solution for desired dose constraints, and thus mayprove to be good starting point in an optimization process.

Then, at process block 1606, updated image information is provided,which may include a multi-modality image set. For example, the updatedimage set may include any number of magnetic resonance images, computedtomography images, ultrasound images, positron emission tomographyimages, and synthetic electron density images, or any combinationsthereof. The updated image information may then be used at process block1608 to generate contours of any updated target volumes. As described,such contours may be generated autonomously or semi-autonomously.

The updated target contours may then be used, at process block 1610, togenerate PCR structures arranged generally in relation to, or about theupdated contours of targeted structures. In some aspects, the PCRstructures may be centered about the updated target contours anddirected towards any or all non-targets, or objects at risk, and pointsalong a dose volume histogram of the PCR structures may be used togenerate an objective function for use in the optimization process.Then, at process block 1612 an optimization process may be performedusing generated PCR structures along with objectives defined inaccordance with determined dose gradients from process block 1604.During the optimization process, which may require any number ofiterations, the objective function may be modified according to definedobjectives, to achieve targeted dose constraints or dose gradients, forexample, dose gradient from surfaces of the updated target volumes inrelation to any or all non-target or objects at risk.

Then, at process block 1614, a report is generated, representative ofthe adapted radiotherapy plan obtained from the plan optimizationprocess, which may take any shape or form, as desired or required by atreatment plan verification or delivery system.

Since, as described, contoured structures may be subject to deformableregistration using standard algorithms, which typically do not providevery accurate and reliable contours, direct usage of deformed contoursmay prove problematic for some daily plan optimization schemes. Bycontrast, the GM algorithm may not require contours to be very accuratesince only the relative positions of the structures may utilized, andvariations in the organ shapes may not affect the accuracy of the PCR.For example, to generate a PCR toward a certain critical structure, onemay only need to know what part of the ring surrounding the target willbe toward that critical structure. Therefore, the PCR area is theprojection of that critical structure to the surface of the target orthe ring. Since the 3D volume information of the critical structure iscollapsed (projected) onto the 2D target surface to determine theportion of the ring that is toward that critical structure, theinaccuracies in the volume of the critical structure are largelydiminished by the collapsing. Moreover, margins may be applied aroundthe ring areas, for example, a margin of 3 mm. For very large structuressuch as bowels, only part of the critical structure that is within acertain distance (e.g. 2 cm) of the target may be included.Additionally, it is possible to generate the PCR for certain organs inan easier way, for example the rectum, where one can have the PCR coverthe whole range of posterior directions from prostate surface.

For some structures, such as a bladder, auto-contouring can achievedaily contours with adequate accuracy. As such, use of PCRs may providesimilar time reduction in terms of contour delineation. However, since abladder volume changes drastically from day to day, this would make theoptimum objective weights in the objective function difficult topredict. Therefore, a PCR-based optimization may still advantageous inthe sense of providing for a more predictable optimization, as mentionedabove.

In a study, the approach of the present disclosure was investigated toquickly generate adaptive plans. Using daily treatment CTs, only targetstructures (CTVs) were delineated. Then PCR structures of uniformthickness, namely 3 mm, were automatically generated using an in-houseprogram. There were separate PCR structures toward each of the importantcritical structures surrounding the target. This was accomplished byfirst generating rings surrounding each target all around, and thenfinding the intersection of the rings with the projection of each OAR.Several ring structures for each OAR were formed, such as PCR#1: from 0to 3 mm, PCR#2: from 3 to 6 mm, PCR#3: from 6 to 9 mm, and so on.

FIG. 17 shows a diagram illustrating an example comparing onlinere-planning schemes using conventional (left) and GM (right) method inthree steps, namely daily image acquisition, contour generation based ondaily images, and plan re-optimization based on new contours. In theconventional method, the full set of contours are needed in Step 2 andthe original objective functions, which can be a complicated set ofdose-volume constraints, are used in the subsequent plan optimization.By contrast, the GM method of the present disclosure provides for asimplified approach with significant planning time savings, whereby onlythe target and PCR contours, which may be generated automatically, areneeded along with determined dose gradients.

Given that initial planning is typically not under the time constraintsof online planning, an original plan may be generated in any fashion,for example, using the original volumes and dose-volume constraints.Such original plan may describe the best plan achievable, which wouldpossess the steepest dose gradients achievable toward each OAR. Using anoriginal plan with a nominally optimum dose distribution, several pointsalong the DVH of generated PCRs were recorded and used to generate anobjective function which as stored for online re-optimization. Theoptimization step was then performed using the newly generated PCRsalong with the stored objective function. To quickly reach dose gradientgoals, the optimization of the adaptive plan started from the originalplan on the image of the day. Since the dose gradients may be the bestachievable, each of the objective goals (for each PCR) was set to theexact DVH points obtained from the original plan, and weighted equally.

This approach, as described, is different from common optimizationpractice, where the DVH objectives are almost always set to points thatare “better than actually desired,” making the optimization processunstable and prone to undesirable results. Since, in the approach of thepresent disclosure, it is known what can be achieved from originalplanning, the DVH goals can be set to those points. The main advantageof this approach is that it eliminates the necessity to delineate thefull set of structures, and only requires the delineation of the target,therefore drastically reduces the re-planning time. Also this approachmakes daily re-planning more reproducible, eliminating the necessity fortrial-and-error effort, since gradients toward each structure areoftentimes achievable (already achieved in an original plan), thereforeadjusting the optimization criteria is not necessary for the dailyanatomy. On the other hand, standard optimization methods rely ondose-volume-based criteria, which may vary drastically from day to day,due to the variation in the volume of the organs.

It is worth taking into consideration the situation when the volume ofthe OAR is very small one day and the original dose gradient maintainedwould lead to unacceptable volume of the organ to receive high doses.Since there are physical limitations to the magnitude of dose gradients,once reached, one cannot increase the gradient any further unlesswilling to sacrifice other plan quality parameters, such as targetcoverage. If the steepness of the gradient for one day can be increasedwithout sacrificing the target coverage, this indicates that the dosegradients in the original plan may not have been as high as possible.Therefore it is beneficial that the original plan quality be indeedoptimum in terms of dose gradients toward critical structures. If so,the dose gradient cannot be increased any further without sacrificingother parameters, and maintaining the original gradient would satisfythe goal of online re-planning.

FIG. 18 shows an example comparing dose distributions from an imageguided radiotherapy (IGRT) repositioning, a conventional full-blownoptimization based on a full contour set, and a gradient-maintenancere-planning, in accordance with the present disclosure. It is clear theGM-based dose distribution, for example dose to duodenum, is equivalentto that from the conventional re-optimization, but is substantiallyimproved from that obtained using repositioning. Furthermore, theplanning time required for the GM method is only 10% of that requiredfor the conventional re-optimization.

FIG. 19 shows an example comparing dose volume histograms for the threestrategies of FIG. 18 used to address inter-fractional variations in 6daily treatments for a prostate cancer case. Once again, it is clear,from the 6 sets of DVHs of the rectum, bladder and planning targetvolume (PTV) (prostate and seminal vesicle) shown, that the GM-basedonline re-planning scheme is equivalent to the conventional full-blownre-optimization, and significantly better than the IGRT repositioningmethod.

Returning to FIG. 1, at process block 16, a number of quality controlprotocols and procedures for the provided radiotherapy plan may beperformed before treatment. As known in the art, such protocols areusually according to treatment systems employed and established clinicalworkflows, providing verification for the accuracy of the delivery asdictated by the treatment plan. Ahead of radiation delivery, a patientmay typically undergo a positional set up and immobilization, andtherapists perform a number of positional verifications and adjustmentsbased on pre-treatment imaging, as necessary. Corrections to patienttreatment parameters performed while the patients is on the treatmenttable, classified as “online” corrections, may involve translational orrotational adjustments, as well as modification of the treatment planusing ART, as described.

Then at process block 18, the radiotherapy treatment is delivered,followed by a step of assessing and/or modifying the treatment, atprocess block 20, to include a report provided by a record and verifysystem. This step may trigger a number of corrective actions made afterthe daily treatment has been delivered affecting the treatments ofsubsequent days. Therefore, in yet another embodiment of the presentdisclosure, systems and methods directed to implementing an offlineoptimization process are provided. The offline optimization processconsists of computing an accumulated dose delivered using a set ofdeformably-registered images from previous fractions, for example dailyimages (CT, MRI or US), and generating an optimized plan for subsequenttreatments to correct for any accumulated residual errors from anynumber of intra-fractional changes, as well other changes not correctedby any online re-planning schemes. For example, residual errors mayoccur during radiation treatment of prostate cancer, since the prostatemay drift systematically or move abruptly due to gas passing through therectum. Such changes may not be accounted for by an online re-planningusing images acquired immediately before treatment.

Specifically, the dose delivered at the any i^(th) fraction using anonline re-planning scheme, as described, may be reconstructed by usingdelivered parameters, for example apertures and monitor units (MU)numbers. The accumulated dose up to that i^(th) fraction may becalculated based on an image of the day using any deformable imageregistration method. Since both the accumulated dose distribution andthe desired dose distribution (for example a gold standard) may betypically generated based on the same image, namely a CT or MRI of theday, the residual errors from intra-fractional changes up to i^(th)fraction not accounted for by previous the online re-planning schemesmay be calculated by simply subtracting the accumulated dosedistribution from the desired dose distribution. Such residual errorsmay be provided as input into a planning system to be treated as aninitial background, the generated background including both positive andnegative numbers. An adaptive optimization with this background mayyield an initial plan for use in an online re-planning at a subsequentfraction. For example, FIG. 20 illustrates an offline optimization, asdescribed, potentially to be used in conjunction with an onlinere-planning method.

The approach of the present disclosure, as described, may be used tocorrect residual errors after an online re-planning of one or multiplefractions. For a subset of patients, it is likely that an onlinere-planning, as described, may leave very little error as compared witha re-optimizing plan. For such cases, an offline adaptive optimizationapproach can be used less often, for example, on a weekly or biweeklybasis, providing correction for accumulated residual errors frommultiple fractions rather than a only previous fraction.

Features suitable for such combinations and sub-combinations would bereadily apparent to persons skilled in the art upon review of thepresent application as a whole. The subject matter described herein andin the recited claims intends to cover and embrace all suitable changesin technology.

1. A method for adapting a radiotherapy treatment plan, the methodcomprising: providing an initial radiotherapy plan to be adaptedaccording to an updated image set, the initial radiotherapy plan havinga radiation dose distribution; determining a plurality of dose gradientsusing the radiation dose distribution; defining an optimizationobjective using the dose gradients; receiving the updated image set;generating, using the updated image set, an updated set of contoursrepresentative of an updated target volume; forming a set of partialrings using the updated set of contours, the set of partial ringsarranged about the updated set of contours representative of the updatedtarget volume; performing a plan optimization using the optimizationobjective and the set of partial rings; and generating a reportrepresentative of an adapted radiotherapy plan obtained using the planoptimization.
 2. The method of claim 1, wherein the plurality of dosegradients define a radiation dose variation from at least one targetstructure toward each of a plurality of non-target structures.
 3. Themethod of claim 1, wherein the updated image set is a multi-modalityimage set comprising at least one of a magnetic resonance image set, acomputed tomography image set, an ultrasound image set, a positronemission tomography image set, and a synthetic image set.
 4. The methodof claim 1, wherein the method further comprises combining at least oneof a magnetic resonance image set, a computed tomography image set, anultrasound image set, a positron emission tomography image set, and asynthetic image set to create the updated image set, using a deformableimage registration.
 5. The method of claim 4, wherein the deformableimage registration includes a parameterized b-spline deformation modelusing a bending energy limited diffeomorphism (BELD) regularizationtechnique.
 6. The method of claim 4, wherein the deformable imageregistration includes a symmetric force Demons technique whereby aGaussian smoothing kernel is adaptively adjusted according to athreshold.
 7. The method of claim 1 wherein the set of partial ringsincludes partial concentric rings centered about the updated set ofcontours representative of the updated target volume and directedtowards a plurality of objects at risk.
 8. The method of claim 7, themethod further comprising determining a plurality of points along a dosevolume histogram of the set of partial rings, the plurality of pointsused to generate an objective function.
 9. The method of claim 8, themethod further comprising modifying the objective function according tothe optimization objective to achieve a plurality of target dosegradients from a surface of the updated target volume in relation toeach of the plurality of objects at risk.
 10. The method of claim 1,wherein the method further comprises determining the initialradiotherapy plan using an offline optimization process, wherein theoffline optimization process uses an accumulated dose from at least oneof a plurality of treatment fractions to generate a backgroundrepresenting a plurality of residual errors accumulated from at leastone of a plurality of intra-fractional changes.
 11. The method of claim1, wherein the updated image set comprises a 4D MR image set or a 4D CTimage set.
 12. The method of claim 11, wherein the initial radiotherapyplan and the adaptive radiation therapy plan are 4D plans generatedbased on the 4D MR image set or 4D CT image set, or both, using asegment aperture morphing (SAM) algorithm.
 13. The method of claim 1,the method further comprising generating a set of isodose contours basedon radiation dose distribution.
 14. The method of claim 1, the methodfurther comprising performing the plan optimization using the isodosecontours and a segment aperture morphing (SAM) algorithm.
 15. A systemfor adapting a radiotherapy treatment plan, the system comprising: aninput configured to receive an updated image set and an initialradiotherapy plan having a radiation dose distribution; a computerconfigured to: determine a plurality of dose gradients using theradiation dose distribution; define an optimization objective using thedose gradients; generate an updated set of contours representative of anupdated target volume using the updated image set received from theinput; form a set of partial rings using the updated set of contours,the set of partial rings arranged about the updated set of contoursrepresentative of the updated target volume; perform a plan optimizationusing the optimization objective and the set of partial rings; andgenerate a report representative of an adapted radiotherapy planobtained using the plan optimization.
 16. The system of claim 15,wherein the computer is further configured to combine at least one of amagnetic resonance image set, a computed tomography image set, anultrasound image set, a positron emission tomography image set, and asynthetic image set to create the updated image set, using a deformableimage registration.
 17. The system of claim 15, wherein the set ofpartial rings includes partial concentric rings centered about theupdated set of contours representative of the updated target volume anddirected towards a plurality of objects at risk.
 18. The system of claim17, wherein the computer is further configured determine a plurality ofpoints along a dose volume histogram of the set of partial rings, theplurality of points used to generate an objective function.
 19. Thesystem of claim 18, wherein the computer is further configured to modifythe objective function according to the optimization objective toachieve a plurality of target dose gradients from a surface of theupdated target volume in relation to each of the plurality of objects atrisk.
 20. The system of claim 15, wherein the computer is furtherconfigured to determine the initial radiotherapy plan using an offlineoptimization process, wherein the offline optimization process uses anaccumulated dose from at least one of a plurality of treatment fractionsto generate a background representing a plurality of residual errorsaccumulated from at least one of a plurality of intra-fractionalchanges.